About Me
Hello! My name is Arthur Chen (陳皓楠). I’m a master’s student at the R2L Lab of the University of Waterloo, advised by Victor Zhong. I am also a researcher at the Vector Institute. During my undergraduate studies, I built tools for retrieval with Jimmy Lin and multimodal retrievers with Wenhu Chen.
Research Interests
I sit at the intersection of Machine Learning (ML) and Natural Language Processing (NLP). To that end, my primary interest is using natural language to enable ML systems (e.g., intelligent agents) to automatically adapt to unseen environments. I’m particularly interested in:
- Language Grounding: using language to establish a shared context between the ML model and the environment.
- Test-time Adaptation: adapting ML models to new environments without human supervision at deployment/test-time.
- Automatic Evaluation: evaluating AI systems’ performance without human annotation.
News
- [Feb 2026]: Agentic AI lightning talk on “Adapting Agents to Unseen Environments” – Remarkable 2026 at Vector Institute.
- [Dec 2025]: Invited talk on “Test-Time Adaptation via Data Synthesis” – Bloomberg CTO Office.
- [May 2025]: Started my internship at Salesforce AI Research to work on agents!
Selected Papers
For update-to-date papers, please refer to Google Scholar.
Test-Time Adaptation for LLM Agents via Environment Interaction
Arthur Chen, Zuxin Liu, Jianguo Zhang, Akshara Prabhakar, Zhiwei Liu, Shelby Heinecke, Silvio Savarese, Victor Zhong, Caiming Xiong
Introduces efficient adaptation strategies for LLM agents to adapt to new environments at test-time via interaction.
International Conference on Learning Representations (ICLR), 2026.
Links: paper • code • project page
Arthur Chen, Zuxin Liu, Jianguo Zhang, Akshara Prabhakar, Zhiwei Liu, Shelby Heinecke, Silvio Savarese, Victor Zhong, Caiming Xiong
Introduces efficient adaptation strategies for LLM agents to adapt to new environments at test-time via interaction.
International Conference on Learning Representations (ICLR), 2026.
Links: paper • code • project page
SynQuE: Estimating Synthetic Dataset Quality Without Annotations
Arthur Chen, Victor Zhong
SynQuE is a framework and benchmark for ranking synthetic datasets by their expected real-world performance without requiring any labeled real data.
International Conference on Learning Representations (ICLR), DATA-FM Workshop, 2026.
Links: paper • code • project page
Arthur Chen, Victor Zhong
SynQuE is a framework and benchmark for ranking synthetic datasets by their expected real-world performance without requiring any labeled real data.
International Conference on Learning Representations (ICLR), DATA-FM Workshop, 2026.
Links: paper • code • project page
UniIR: Training and Benchmarking Universal Multimodal Information Retrievers
Cong Wei, Yang Chen, Arthur Chen, Hexiang Hu, Ge Zhang, Jie Fu, Alan Ritter, Wenhu Chen
UniIR is an instruction-guided multimodal retriever for eight retrieval tasks, evaluated by the standardized M-BEIR benchmark.
European Conference on Computer Vision (ECCV), Oral Presentation 2024.
Links: paper • code • project page
Cong Wei, Yang Chen, Arthur Chen, Hexiang Hu, Ge Zhang, Jie Fu, Alan Ritter, Wenhu Chen
UniIR is an instruction-guided multimodal retriever for eight retrieval tasks, evaluated by the standardized M-BEIR benchmark.
European Conference on Computer Vision (ECCV), Oral Presentation 2024.
Links: paper • code • project page
VideoScore: Building Automatic Metrics to Simulate Fine-grained Human Feedback for Video Generation
Xuan He, Dongfu Jiang, Ge Zhang, Max Ku, Achint Soni, Sherman Siu, Arthur Chen, et al.
VideoScore is an automatic metric for AI-generated videos that simulates detailed human feedback to predict quality scores.
Empirical Methods in Natural Language Processing (EMNLP), 2024.
Links: paper • code • project page
Xuan He, Dongfu Jiang, Ge Zhang, Max Ku, Achint Soni, Sherman Siu, Arthur Chen, et al.
VideoScore is an automatic metric for AI-generated videos that simulates detailed human feedback to predict quality scores.
Empirical Methods in Natural Language Processing (EMNLP), 2024.
Links: paper • code • project page